Seasonal sources and sinks of atmospheric CO&lt;sub&gt;2&lt;/sub&gt;
                        Direct inversion of filtered data

Enting, I. G.; Mansbridge, J. V.

doi:10.3402/tellusb.v41i2.15056

Cited by 92 publications

(63 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus far, two techniques have been used to infer optimal air-sea fluxes of CO 2 from ocean interior C ant data: a basis function approach and an ensemble Kalman filter (EnKF) technique. The basis function approach is analogous to a method that has been widely used to estimate sources and sinks of atmospheric CO 2 based on atmospheric observations and atmospheric transport models (e.g., Enting and Mansbridge, 1989;Tans et al, 1990;Bousquet et al, 2000). This method has been adapted to estimate air-sea fluxes of heat , oxygen , and CO 2 (Gloor et al, 2003;Mikaloff Fletcher et al, 2006;Mikaloff Fletcher et al, 2007;Gruber et al, 2009) using ocean interior observations and OGCMs.…”

Section: The Inverse Approachmentioning

confidence: 99%

Global ocean storage of anthropogenic carbon

et al. 2013

View full text Add to dashboard Cite

The global ocean is a significant sink for anthropogenic carbon (C_ant), absorbing roughly a third of human CO₂ emitted over the industrial period. Robust estimates of the magnitude and variability of the storage and distribution of C_ant in the ocean are therefore important for understanding the human impact on climate. In this synthesis we review observational and model-based estimates of the storage and transport of C_ant in the ocean. We pay particular attention to the uncertainties and potential biases inherent in different inference schemes. On a global scale, three data-based estimates of the distribution and inventory of C_ant are now available. While the inventories are found to agree within their uncertainty, there are considerable differences in the spatial distribution. We also present a review of the progress made in the application of inverse and data assimilation techniques which combine ocean interior estimates of C_ant with numerical ocean circulation models. Such methods are especially useful for estimating the air–sea flux and interior transport of C_ant, quantities that are otherwise difficult to observe directly. However, the results are found to be highly dependent on modeled circulation, with the spread due to different ocean models at least as large as that from the different observational methods used to estimate C_ant. Our review also highlights the importance of repeat measurements of hydrographic and biogeochemical parameters to estimate the storage of C_ant on decadal timescales in the presence of the variability in circulation that is neglected by other approaches. Data-based C_ant estimates provide important constraints on forward ocean models, which exhibit both broad similarities and regional errors relative to the observational fields. A compilation of inventories of C_ant gives us a "best" estimate of the global ocean inventory of anthropogenic carbon in 2010 of 155 ± 31 PgC (±20% uncertainty). This estimate includes a broad range of values, suggesting that a combination of approaches is necessary in order to achieve a robust quantification of the ocean sink of anthropogenic CO₂

show abstract

Section: The Inverse Approachmentioning

confidence: 99%

Global ocean storage of anthropogenic carbon

et al. 2013

View full text Add to dashboard Cite

show abstract

“…elling can be used to estimate the magnitude of sources and sinks of CO 2 at different temporal and spatial scales (Enting and Mansbridge, 1989;Rayner et al, 1999;Rödenbeck et al, 2003;Chevallier et al, 2010). This method relies on precision measurements of atmospheric CO 2 concentrations to refine the prior estimates of the CO 2 fluxes.…”

Section: A Nickless Et Al: Greenhouse Gas Network Design South Africamentioning

confidence: 99%

Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case

Nickless

Ziehn

Scholes³

et al. 2015

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did.Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study.

show abstract

“…Enting and Mansbridge, 1989;Rayner et al, 1999;Rödenbeck et al, 2003;Chevallier et al, 2010), employing mainly flask and in situ data. These observations include measurements from surface stations, tall towers, air planes and ships.…”

Section: Introductionmentioning

confidence: 99%

Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

et al. 2014

View full text Add to dashboard Cite

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CA-BLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30 %. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50 %, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

show abstract

Seasonal sources and sinks of atmospheric CO<sub>2</sub> Direct inversion of filtered data

Cited by 92 publications

References 34 publications

Global ocean storage of anthropogenic carbon

Global ocean storage of anthropogenic carbon

Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case

Greenhouse gas network design using backward Lagrangian particle dispersion modelling − Part 1: Methodology and Australian test case

Contact Info

Product

Resources

About